In this paper, we present a comprehensive study of different Independent Component Analysis (ICA) algorithms
for the calculation of coherency and sharpness of electroencephalogram (EEG) signals, in order to
investigate the possibility of early detection of Alzheimer’s disease (AD). We found that ICA algorithms can
help in the artifact rejection and noise reduction, improving the discriminative property of features in high frequency
bands (specially in high alpha and beta ranges). In addition to different ICA algorithms, the optimum
number of selected components is investigated, in order to help decision processes for future works
